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Joint Genetic Analysis of Gene Expression Data with Inferred Cellular Phenotypes

Leopold Parts, Oliver Stegle, John Winn, and Richard Durbin


Even within a defined cell type, the expression level of a gene differs in individual samples. The effects of genotype, measured factors such as environmental conditions, and their interactions have been explored in recent studies. Methods have also been developed to identify unmeasured intermediate factors that coherently influence transcript levels of multiple genes. Here, we show how to bring these two approaches together and analyse genetic effects in the context of inferred determinants of gene expression. We use a sparse factor analysis model to infer hidden factors, which we treat as intermediate cellular phenotypes that in turn affect gene expression in a yeast dataset. We find that the inferred phenotypes are associated with locus genotypes and environmental conditions and can explain genetic associations to genes in trans. For the first time, we consider and find interactions between genotype and intermediate phenotypes inferred from gene expression levels, complementing and extending established results.


Publication typeArticle
Published inPLoS Genetics

Previous versions

Oliver Stegle, Leopold Parts, Richard Durbin, and John Winn. A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies, PLoS Computational Biology, PLoS Computational Biology (Public Library of Science Computational Biology), , 6 May 2010.

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